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Harnessing years of research, CellSage offers an advanced platform for modeling, simulating, and analyzing lithium-ion battery health. This tool, developed through collaboration with the Idaho National Laboratory, delivers comprehensive insights into battery performance, extending its utility throughout the entire battery lifecycle. With a blend of data-driven and physics-based modeling, CellSage provides robust diagnostics and prognostics for various applications such as electric vehicles and consumer electronics. This software simplifies complex battery analysis through a user-friendly graphical interface, presenting accurate lifespan evaluations and capacity fade analysis. Its extensive battery library includes pre-defined types and can be expanded to incorporate new chemistries. CellSage supports operational optimization by simulating different thermal management scenarios, ensuring precise battery management under varying conditions. Key applications of CellSage encompass integrated vehicle health monitoring, supporting early fault detection to improve reliability and longevity. By enhancing condition-based maintenance and prognostic health management, CellSage is pivotal for industries aiming to extend battery life and reduce R&D costs effectively.
The Advanced Electrolyte Model (AEM), developed by Idaho National Laboratory and exclusively distributed by Ridgetop Group, offers a cutting-edge molecular simulation tool for enhancing electrolyte chemistry optimization. Acting as an extensive toolkit, this virtual laboratory aids researchers in fine-tuning electrolyte properties, providing over 100 detailed metrics per run for superior insights into electrolyte behavior. Comprised of a comprehensive database with more than 50 solvents and 30 salts, AEM empowers users to explore vast combinations for optimized solutions within battery systems. Meshing scientific rigor with groundbreaking applications, AEM employs the Nonprimitive, Nonrestricted Associated Mean Spherical Approximation augmented by an ion-solvation equation of state. This combination ensures predictions are closely aligned with laboratory data, typically deviating by merely 5-10%. This high level of accuracy has positioned AEM as an essential tool for global transition efforts from fossil fuel reliance, facilitating advances in electric vehicle adoption and grid-scale battery deployments. Through AEM, scientists receive unparalleled clarity and certainty in qualifying electrolytes, driving forward the clean energy revolution. With the capability to rapidly screen a wide range of materials, AEM is indispensable in expediting the development of new battery designs, curtailing time-to-market and drastically cutting laboratory costs. By efficiently bridging the gap between molecular theory and practical applications, AEM revolutionizes how energy is stored and utilized globally, all the while charting the course for a sustainable energy future.
Sentinel Motion for Rail provides a comprehensive IoT-based system for monitoring critical rail equipment, focusing on temperature, linear, rotational, and vibrational forces. Adapted from its original use in helicopter gearboxes, Sentinel Motion offers a complete toolbox for detecting and analyzing faults in the railway sector, with a particular focus on track, wheel, and bearing conditions. Utilizing a combination of RotoSense smart sensors and the Sentinel Gateway, this system captures real-time data, enabling continuous condition monitoring alongside automated alerts for any detected anomalies. Sentinel Motion helps operators mitigate the high costs associated with bearing and wheel failures by integrating advanced diagnostic tools that deliver key insights for maintenance decisions. Relying on field-proven technology, Sentinel Motion systems are designed to facilitate seamless implementation, with additional software applications that offer comprehensive asset management views. These instruments are adapted for extreme environments, supporting enhanced predictive maintenance strategies and significantly reducing operational costs.
The Adaptive Remaining Useful Life Estimator (ARULE) stands as a sophisticated predictive analytics engine employed for determining the prognostic health of complex systems. Capable of calculating key metrics such as Remaining Useful Life (RUL), State-of-Health (SoH), and Prognostic Horizon (PH), ARULE leverages diverse condition-based feature data to provide timely maintenance alerts. This function allows for precise scheduling of services before reaching critical deterioration thresholds. ARULE’s utility extends across various applications, utilizing an intuitive graphical user interface for easy processing and visualization of prognostic estimates. It integrates condition-based data streams, further supported by user-defined parameters, to output insightful predictions. This versatility enables ARULE to address electrical, mechanical, and electro-mechanical fatigue both efficiently and accurately. By associating with Ridgetop's Sentinel Suite, ARULE gains additional capabilities, particularly in complex systems such as power supply and battery management systems, gearbox and actuator control systems, and industrial automation. These features make ARULE an invaluable component for industries seeking innovative and reliable health management solutions.
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